
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns 
import os


files=os.listdir(r"C:\Users\Nicholas Ra\Covid-19")
files

['country_wise_latest.csv',
 'covid_19_clean_complete.csv',
 'day_wise.csv',
 'full_grouped.csv',
 'usa_country_wise.csv',
 'worldometer_data.csv']


def read_data(path,filename):
    return pd.read_csv(path+'/'+filename)


path=r"C:\Users\Nicholas Ra\Covid-19"
world_data=read_data(path,'worldometer_data.csv')


world_data.head()


day_wise=read_data(path,files[2])


group_data=read_data(path,files[3])


usa_data=read_data(path,files[4])


province_data=read_data(path,files[1])


province_data.shape

(49068, 10)


world_data.head()


world_data.columns

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')


conda install -c plotly plotly_express==0.4.0

Collecting package metadata (current_repodata.json): ...working... done
Note: you may need to restart the kernel to use updated packages.

Solving environment: ...working... done

## Package Plan ##

  environment location: C:\Anaconda3

  added / updated specs:
    - plotly_express==0.4.0


The following packages will be downloaded:

    package                    |            build
    ---------------------------|-----------------
    plotly-4.14.3              |             py_0         5.9 MB  plotly
    plotly_express-0.4.0       |             py_0           5 KB  plotly
    ------------------------------------------------------------
                                           Total:         5.9 MB

The following NEW packages will be INSTALLED:

  plotly             plotly/noarch::plotly-4.14.3-py_0
  plotly_express     plotly/noarch::plotly_express-0.4.0-py_0
  retrying           pkgs/main/noarch::retrying-1.3.3-py_2


Downloading and Extracting Packages

plotly-4.14.3        | 5.9 MB    |            |   0% 
plotly-4.14.3        | 5.9 MB    | ########## | 100% 
plotly-4.14.3        | 5.9 MB    | ########## | 100% 

plotly_express-0.4.0 | 5 KB      |            |   0% 
plotly_express-0.4.0 | 5 KB      | ########## | 100% 
plotly_express-0.4.0 | 5 KB      | ########## | 100% 
Preparing transaction: ...working... done
Verifying transaction: ...working... done
Executing transaction: ...working... done


import plotly.express as px


columns=['TotalCases','TotalDeaths','TotalRecovered','ActiveCases']
for i in columns:
    fig=px.treemap(world_data.iloc[0:20],values=i,path=['Country/Region'],title='Treemap representative of different countries w.r.t to their{}'.format(i))
    fig.show()


day_wise.head()


day_wise.columns

Index(['Date', 'Confirmed', 'Deaths', 'Recovered', 'Active', 'New cases',
       'New deaths', 'New recovered', 'Deaths / 100 Cases',
       'Recovered / 100 Cases', 'Deaths / 100 Recovered', 'No. of countries'],
      dtype='object')


px.line(day_wise,x="Date",y=['Confirmed','Deaths','Recovered','Active'],title='covid cases w.r.t to date',template='plotly_dark')


world_data.head()


population_test_ratio=world_data['Population']/world_data['TotalTests'].iloc[0:20]


fig=px.bar(world_data.iloc[0:20],x='Country/Region',y=population_test_ratio[0:20],color='Country/Region',title='Population to tests done ratio')
fig.show()


world_data.columns

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')


fig=px.bar(world_data.iloc[0:6],x='Country/Region',y=['Serious','Critical','TotalDeaths','TotalRecovered','ActiveCases','TotalCases'])
fig.show()


fig=px.bar(world_data.iloc[0:20],y='Country/Region',x='TotalCases',color='TotalCases',text='TotalCases')
fig.update_layout(template='plotly_dark',title_text='Top 20 countries of total confirmed cases')
fig.show()


world_data.sort_values(by='TotalDeaths',ascending=False)


fig=px.bar(world_data.sort_values(by='TotalDeaths',ascending=False)[0:20],y='Country/Region',x='TotalDeaths',color='TotalDeaths',text='TotalDeaths')
fig.update_layout(template='plotly_dark',title_text='Top 20 countries of total deaths cases')
fig.show()


world_data.columns

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')


fig=px.bar(world_data.sort_values(by='ActiveCases',ascending=False)[0:20],y='Country/Region',x='ActiveCases',color='ActiveCases',text='ActiveCases')
fig.update_layout(template='plotly_dark',title_text='Top 20 countries of total active cases')
fig.show()


fig=px.bar(world_data.sort_values(by='TotalRecovered',ascending=False)[0:20],y='Country/Region',x='TotalRecovered',color='TotalRecovered',text='TotalRecovered')
fig.update_layout(template='plotly_dark',title_text='Top 20 countries of total recovered cases')
fig.show()


world_data.head()


labels=world_data[0:15]['Country/Region'].values
cases=['TotalCases','TotalDeaths','TotalRecovered','ActiveCases']
for i in cases:
    fig=px.pie(world_data[0:15],values=i,names=labels,hole=0.3,title="{}recorded w.r.t to WHO region of 15 worst affected countries".format(i))
    fig.show()


world_data.head()


deaths_to_confirmed=world_data['TotalDeaths']/world_data['TotalCases']
deaths_to_confirmed

0      0.032353
1      0.033810
2      0.020558
3      0.016752
4      0.017845
         ...   
204    0.076923
205         NaN
206         NaN
207         NaN
208    0.100000
Length: 209, dtype: float64


px.bar(world_data,x='Country/Region',y=deaths_to_confirmed,title='Deaths to confirmed ratio of worst affected countries')


deaths_to_recovered=world_data['TotalDeaths']/world_data['TotalRecovered']


px.bar(world_data,x='Country/Region',y=deaths_to_confirmed,title='Deaths to recovered ratio of worst affected countries')


world_data['Serious,Critical']/world_data['TotalDeaths']

0      0.112381
1      0.084323
2      0.214804
3      0.157470
4      0.056122
         ...   
204         NaN
205         NaN
206         NaN
207         NaN
208         NaN
Length: 209, dtype: float64


px.bar(world_data,x='Country/Region',y=world_data['Serious,Critical']/world_data['TotalDeaths'],title='Serious/Critical to deaths ratio of worst affected countries')


group_data.head()


from plotly.subplots import make_subplots
import plotly.graph_objects as go


def country_visualization(group_data,country):
    data=group_data[group_data['Country/Region']==country]
    
    df=data.loc[:,['Date','Confirmed','Deaths','Recovered','Active']]
    
    fig = make_subplots(rows=1,cols=4,subplot_titles=('Confirmed','Active','Recovered','Deaths'))
    
    fig.add_trace(
        go.Scatter(name='Confirmed',x=df['Date'],y=df['Confirmed']),row=1,col=1)
    
    fig.add_trace(
        go.Scatter(name='Deaths',x=df['Date'],y=df['Deaths']),row=1,col=2)
    
    fig.add_trace(
        go.Scatter(name='Recovered',x=df['Date'],y=df['Recovered']),row=1,col=3)
    
    fig.add_trace(
        go.Scatter(name='Active',x=df['Date'],y=df['Active']),row=1,col=4)
    
    fig.update_layout(height=600,width=1000,title_text='Date vs Recorded cases of {}'.format(country),template="plotly_dark")
    fig.show()


# The next line of code is for automating
country_visualization(group_data, 'Brazil')


country_visualization(group_data, 'US')


country_visualization(group_data, 'Romania')


country_visualization(group_data, 'Russia')

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa

	Date	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	No. of countries
0	2020-01-22	555	17	28	510	0	0	0	3.06	5.05	60.71	6
1	2020-01-23	654	18	30	606	99	1	2	2.75	4.59	60.00	8
2	2020-01-24	941	26	36	879	287	8	6	2.76	3.83	72.22	9
3	2020-01-25	1434	42	39	1353	493	16	3	2.93	2.72	107.69	11
4	2020-01-26	2118	56	52	2010	684	14	13	2.64	2.46	107.69	13

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
5	Mexico	North America	1.290662e+08	462690	6590.0	50517.0	819.0	308848.0	4140.0	103325.0	3987.0	3585.0	391.0	1056915.0	8189.0	Americas
11	UK	Europe	6.792203e+07	308134	NaN	46413.0	NaN	NaN	NaN	NaN	73.0	4537.0	683.0	17515234.0	257873.0	Europe
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
202	Saint Kitts and Nevis	North America	5.323700e+04	17	NaN	NaN	NaN	16.0	NaN	1.0	NaN	319.0	NaN	1146.0	21526.0	Americas
203	Greenland	North America	5.678000e+04	14	NaN	NaN	NaN	14.0	NaN	0.0	NaN	247.0	NaN	5977.0	105266.0	Europe
205	Caribbean Netherlands	North America	2.624700e+04	13	NaN	NaN	NaN	7.0	NaN	6.0	NaN	495.0	NaN	424.0	16154.0	NaN
206	Falkland Islands	South America	3.489000e+03	13	NaN	NaN	NaN	13.0	NaN	0.0	NaN	3726.0	NaN	1816.0	520493.0	NaN
207	Vatican City	Europe	8.010000e+02	12	NaN	NaN	NaN	12.0	NaN	0.0	NaN	14981.0	NaN	NaN	NaN	Europe

	Date	Country/Region	WHO Region
0	2020-01-22	Afghanistan	Eastern Mediterranean
1	2020-01-22	Albania	Europe
2	2020-01-22	Algeria	Africa
3	2020-01-22	Andorra	Europe
4	2020-01-22	Angola	Africa

Data Analysis on Covid-19 in 2020¶